Brief Description Deep Learning is revolutionizing both Data Science and
Artificial Intelligence real-world applications. Yet, being the
discipline so young, it’s not straightforward to understand both the
reasoning at its core and its countless use cases. In this talk we will
review the basics of Neural Networks, from the classical CNNs to the
current state of the art, comparing them through real industry
applications and highlighting pros and cons in a business setting.

Abstract / Summary Deep Learning has been on a hype roll for a few
years. Being such a young discipline makes deep learning interesting,
but also subject to misunderstandings. Every year, brand new
architectures rise, taking over old ones and outperforming state of the
art benchmarks for accuracy. Further, the applications of deep learning
are at the core of some of the most advanced technologies like
autonomous driving, personal assistants, and customer profiling. In such
a context it is not straightforward to grasp what is at the core of deep
learning itself, and what is common to all the architectures, neither to
realize how concrete use cases can be tackled. Using Python, we’ll take
the audience from the simplest neuron, the atom of the deep learning
world, to the most recent architectures. We’ll achieve this using a
simple Convolutional Neural Network as a building block and comparing
that to the latest breakthroughs in image recognition. In doing this
we’ll try to give an answer to the following questions: • Is deep
learning actually useful in a business setting? • What about state of
the art techniques in the Computer Vision field: are we just stacking
more and more convolutional and pooling layers? We will then address
real industry applications (e.g. the insurance sector) using analyzed
techniques. This will include opening some of these so- called black box
models and retraining them, at least partially, on our datasets, or
building a complete brand new network from scratch, tailoring it
according to the application needs and datasets characteristics.